MLRC VisionLab
Designing Systems for Multilingual Learners
Join the VisionLab Community of Practice!
The MLRC VisionLab is a new MLRC initiative that provides district ML leaders an opportunity to work together and with other districts to improve programming for multilingual learners.
Join MLRC and district facilitators in a year-long community of practice to jointly examine how to use systems change frameworks and multilingual learning frameworks to plan for and implement changes to multilingual learner programming across your district.
Each VisionLab cohort includes:
Participation for team of up to four district educators
Online community of practice with district colleagues
Facilitation by research and practitioner experts
Opportunity to engage in Action Research Cycle
Development of district ML action plan
1:1 follow-up coaching
Optional full-day district site visit with MLRC experts
Systems change process aligned with ML frameworks
VisionLab begins with Systems Change and Evidence-Based Frameworks:
Evidence-Based ML Frameworks
Each cohort uses MLRC Multilingual Learner Program Framework and relevant State ML Frameworks.
MLRC Multilingual Learner Program Framework
Program Structure including percentage of instructional time dedicated to each language and staffing qualifications
High-quality Curriculum in both languages
High-quality Instructional Practices
Assessment for evaluating student progress in both languages
Educator Capacity including educator preparation, high standards, resources
Family and Community Engagement
Systems Alignment, Funding and Leadership to assure ML focus is integrated across systems
Culture/Asset Orientation/Shared Responsibility
Data Use for both instruction and accountability
Sample VisionLab Agenda
Building the VisionLab Cohort
- Using a systems change framework
- Choosing an evidence-based ML framework
- Thinking about a problem of practice
- Who should be at the table?
- Defining your goals
Why Action Research?
- Designing and using action research to answer questions
- Reviewing action research examples
- Planning your research
Identifying Data to Understand ML Students and Programming
- Brainstorming data sources
- Ensuring all voices are heard
- Planning data collection
Reviewing Action Research Results
- What next? What voices are still needed?
- Deciding priorities
- Connecting data to frameworks
Working on an Action Plan
- Identifying and choosing change approaches
- Sharing with the community
- Reviewing goals
Ready for Next Steps
- Presenting your plan to your cohort
- Planning for continuous improvement process
- Identifying next steps, possible challenges